Subdomain 2.2: Perform Feature Engineering
| Exam Objective | Chapter |
|---|---|
| 2.2-1. Identify and extract features from datasets, including from data sources such as text, speech, image, public datasets, etc. | 7 |
| 2.2-2. Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, One-hot encoding, reducing dimensionality of data) | 7 |
Subdomain 2.3: Analyze and Visualize Data for Machine Learning
| Exam Objective | Chapter |
|---|---|
| 2.3-1. Graphing (scatter plot, time series, histogram, box plot) | 9 |
| 2.3-2. Interpreting descriptive statistics (correlation, summary statistics, p value) | 9 |
| 2.3-3. Clustering (hierarchical, diagnosing, elbow plot, cluster size) | 9 |
Domain 3: Modeling
Subdomain 3.1: Frame Business Problems as Machine Learning Problems
| Exam Objective | Chapter |
|---|---|
| 3.1-1. Determine when to use/when not to use ML | 3 |
| 3.1-2. Know the difference between supervised and unsupervised learning | 4 |
| 3.1-3. Selecting from among classification, regression, forecasting, clustering, recommendation, etc. | 4 |
Subdomain 3.2: Select the Appropriate Model(s) for a Given Machine Learning Problem
| Exam Objective | Chapter |
|---|---|
| 3.2-1. XGBoost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning | 8 |
| 3.2-2. Express intuition behind models | 8 |
Subdomain 3.3: Train Machine Learning Models
| Exam Objective | Chapter |
|---|---|
| 3.3-1. Train validation test split, cross-validation | 6 |
| 3.3-2. Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability, etc. | 8 |
| 3.3-3. Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark] | 12, 16 |
| 3.3-4. Model updates and retraining | 8, 12 |
Subdomain 3.4: Perform Hyperparameter Optimization
| Exam Objective | Chapter |
|---|---|
| 3.4-1. Regularization | 8 |
| 3.4-2. Cross validation | 9 |
| 3.4-3. Model initialization | 8 |
| 3.4-4. Neural network architecture (layers/nodes), learning rate, activation functions | 8 |
| 3.4-5. Tree-based models (# of trees, # of levels) | 8 |
| 3.4-6. Linear models (learning rate) | 8 |
Subdomain 3.5: Evaluate machine learning models
| Exam Objective | Chapter |
|---|---|
| 3.5-1. Avoid overfitting/underfitting (detect and handle bias and variance | 9 |
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3.5-2.
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